Authorized Authorized

132 WORLD DEVELOPMENT REPORT 2016
Figure 2.26 The key policy challenge: Adapting the
skills agenda to expected labor market disruptions
Expected labor market disruption and quality-adjusted years of education
Adaptability
(quality-adjusted years of education)
13
Average expected
labor market disruption
FIN
NLD
5.6
IRL BEL
GBR
DEU
NOR
EST
ISL
CYP AUS
DNK
SWE
MYS
SVN MLT
LUX
FRA
LTU
LVA
CZE
ISR
AUT
ESP HRV ROU
HUN
POL
CHL PRT
SVK UKR
CRI
SYC
BGR
Average
MUS
ITA
ALB
GEO
MKD
quality-adjusted
SRB GRC
TJK PAN
CHN
years of education
UZBURYARG
MNG THA
SLV ECU
IND TUR
MEX
ETH
KGZ ZAF BOL
NPL
BGD
KHM
DOM
GTM NIC NGA
PRY
0
AGO
Low (0) Expected labor market disruption High (1)
GDP income group
High Upper-middle Lower-middle Low
Sources: WDR 2016 team, based on STEP surveys (World Bank, various years); Central Asia World Bank
Skills surveys (World Bank, various years); SHIP (World Bank, various years); SEDLAC (Cedlas and
the World Bank); SARMD (World Bank, various years); ECAPOV (World Bank, various years); EAPPOV
(World Bank, various years); the National Bureau of Statistics of China (various years); ILO Laborsta
database (various years); World Development Indicators (World Bank, various years); World Economic
Forum’s Competitiveness Index (WEF, various years). Data at http://bit.do/WDR2016-Fig2_26.
Note: Labor market disruption is an index that goes from 0 (no disruption) to 1 (highest disruption). It is
the standardized summation of two components, equally weighted: the probability of an average job
being computerized (Frey and Osborne 2013, and adjusting for adoption lags), and the intensity of ICT use
at work. For each country, the ICT intensity of employment corresponds to the average for countries at
the next level of development, to be more forward-looking. The quality-adjusted years of education are
constructed by adjusting average years of education for each country with the World Economic Forum’s
quality-of-education indicator. For example, if a country has, on average, 10 years of education and
scores 3.5 on the indicator (which ranges from 0 to 7), its quality-adjusted years of education are 5. See
Monroy-Taborda, Moreno, and Santos, forthcoming, for the WDR 2016. GDP = gross domestic product.
CHE
• Labor supply. The higher the skill requirements for a
job, the more difficult it is for new workers to enter
that market. So, higher demand for workers would
translate into higher wages. If, however, it is easy to
retrain for a new job or skill requirements are low,
there can be downward pressure on wages because
of increased competition. Workers in nonroutine
cognitive occupations are likely to see their higher
productivity rewarded as higher earnings because
entry barriers are high. But low-skilled workers
in nonroutine manual occupations are likely to
see their earnings fall over time as middle-skilled
workers in routine occupations are displaced and
start competing for the available jobs in low-paying
occupations (table 2.5). 157
Therefore, the main winners from technological
change will have and use new economy skills and gain
employment in nonroutine cognitive occupations.
Table 2.5 Expected impacts of
technological change on employment
and earnings
Type of occupation
(by skills intensity)
Nonroutine
cognitive
Routine cognitive
and manual
Expected impact on
Employment Earnings
Positive Positive
Negative
Negative
Nonroutine manual Positive Negative
Source: WDR 2016 team, based on Autor 2014.
The young, the better educated, and those already
better off are most likely to benefit from digital technologies—with
older workers, those with less education,
and the poor falling behind. The former group is
more likely to have more advanced skills—especially
cognitive and ICT skills—regardless of their occupation
or work status. 158 In addition, these groups
are disproportionally likely to be in, or to move into,
occupations that pay well and are likely to grow in the
future—those intensive in nonroutine skills (figure
2.27). 159 Recent evidence from the United States shows
that there has been a marked decline in the rate at
which workers transition into routine employment
(particularly among the young) but that women and
those with higher education levels have found it
easier to adjust to these changes by moving into the
high-paying, nonroutine cognitive jobs. 160
A big challenge for policy makers, especially in rapidly
aging societies, is managing skill obsolescence.
Recall that the surge in the demand for new economy
skills has been concentrated among young workers
(see figure 2.21). Digital technologies accelerate the
depreciation of skills and work experience, affecting
especially older workers (box 2.9). But obsolescence is
not destiny or the same for all types of skills. Most literature
argues that younger workers have a comparative
advantage in tasks where problem solving, learning,
and speed are important (“fluid” abilities), and older
workers have an advantage when experience and verbal
abilities matter more (“crystallized” abilities). 161 But
evidence from Germany shows that workers in their
50s experienced a more rapid growth in tasks intense
in fluid cognitive skills than those in their 30s. 162 Box
2.10 examines the gender impacts in more depth.
A policy agenda
Digital technologies improve overall welfare and can
reduce poverty, but without complementary policies,
many benefits can go unrealized and inequality can
increase. To capitalize on the benefits—and to do